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arXiv 2024年

作者： Yang, Ziduo Zhao, Yi-Ming Wang, Xian Liu, Xiaoqing Zhang, Xiuying Li, Yifan Lv, Qiujie Chen, Calvin Yu-Chian Shen, Lei Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Physics National University of Singapore Singapore117551 Singapore -Preferred Program School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong L-Med Biotechnology Co. Ltd. Guangdong Meizhou514699 China Research Institute Chongqing401123 China

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://***/Shen-Group/DeepRelax. Copyright © 2024, The Authors. All rights reserved.

关键词： Point defects

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Inhibition of dengue viruses by N-methylcytisine thio derivatives through targeting viral envelope protein and NS2B-NS3 protease

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Bioorganic and Medicinal Chemistry Letters 2024年 99卷 129623页

作者： Lin, Chen-Sheng Lu, Chih-Hao Lin, Tsai-Hsiu Kiu, Yan-Tung Kan, Ju-Ying Chang, Yu-Jen Hung, Ping-Yi Tsypyshev, Dmitry O. Tsypysheva, Inna P. Lin, Cheng-Wen Division of Gastroenterology Kuang Tien General Hospital No. 117 Shatian Rd Shalu District Taichung City 433 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu City Taiwan Department of Medical Laboratory Science and Biotechnology China Medical University Taichung 40402 Taiwan Department of Laboratory Medicine China Medical University Hospital Taichung 40402 Taiwan The Ph.D. Program of Biotechnology and Biomedical Industry China Medical University Taichung Taiwan Ufa Institute of Chemistry Ufa Federal Research Centre of the Russian Academy of Sciences 71 prosp. Oktyabrya Ufa 450054 Russian Federation Department of Medical Laboratory Science and Biotechnology Asia University Taichung41354 Taiwan

Dengue virus (DENV) is a significant global health threat, causing millions of cases worldwide each year. Developing antiviral drugs for DENV has been a challenging endeavor. Our previous study identified anti-DENV properties of two (-)-cytisine derivatives contained substitutions within the 2-pyridone core from a pool of 19 (-)-cytisine derivatives. This study aimed to expand on the previous research by investigating the antiviral potential of N-methylcytisine thio (mCy thio) derivatives against DENV, understanding the molecular mechanisms of antiviral activity for the active thio derivatives. The inhibitory assays on DENV-2-induced cytopathic effect and infectivity revealed that mCy thio derivatives 3 ((1R,5S)-3-methyl-1,2,3,4,5,6-hexahydro-8H-1,5-methanopyrido[1,2-a][1,5]diazocine-8-thione) and 6 ((1S,5R)-3-methyl-2-thioxo-1,2,3,4,5,6-hexahydro-8H-1,5-methanopyrido[1,2-a][1,5]diazocin-8-one) were identified as the active compounds against both DENV-1 and DENV-2. Derivative 6 displayed robust antiviral activity against DENV-2, with EC₅₀ values ranging from 0.002 to 0.005 μM in different cell lines. Derivative 3 also exhibited significant antiviral activity against DENV-2. The study found that these compounds are effective at inhibiting DENV-2 at both the entry stage (including virus attachment) and post-entry stages of the viral life cycle. The study also investigated the inhibition of the DENV-2 NS2B-NS3 protease activity by these compounds. Derivative 6 demonstrated notably stronger inhibition compared to mCy thio 3, revealing its dual antiviral action at both the entry and post-entry stages. Molecular docking simulations indicated that mCy thio derivatives 3 and 6 bind to the domain I and III of the DENV E protein, as well as the active of NS2B-NS3 protease, suggesting their molecular interactions with the virus. The study demonstrates the antiviral efficacy of N-methylcytisine thio derivatives against DENV. It provides valuable insights into the pot

关键词： Antiviral Attachment Dengue virus E protein Entry N-methylcytisine thio derivative NS2B-NS3 protease Post-entry

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Proceedings of the 16th Annual UT-KBRIN bioinformatics Summit 2016: proceedings

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BMC Proceedings 2017年第9期11卷 1-6页

作者： L. Leon Dent Sammed N. Mandape Siddharth Pratap Jianan Dong Jamaine Davis Jennifer A. Gaddy Kofi Amoah Steve Damo Dana R. Marshall Jacob Jones Toni Brandt Gilberto Diaz Qingguo Wang Todd Gary Ashwini Yenamandra Marina Z. Ghattas Marwa Elrakaiby Ramy K. Aziz Hamdallah H. Zedan Moamen Elmassry Marwa ElRakaiby Mariam Lotfy Jarrad Marcel Rania A. Khattab Maha M. Abdelfattah Jack A. Gilbert Pouya Dini Shavahn C. Loux Kirsten E. Scoggin Alejandro Esteller-Vico Edward L. Squires Mats H. T. Troedsson Barry A. Ball Peter Daels Kalpani De Silva Ernest Bailey Joel C. Stephens Theodore S. Kalbfleisch Christine E. Dolin Lauren G. Poole Gavin E. Arteel Michael L. Merchant Daniel W. Wilkey Michelle T. Barati Eric C. Rouchka Richard M. Higashi Teresa W-M Fan Hunter Moseley Andrew N Lane Department of Surgery Meharry Medical College Nashville TN 37208 USA Bioinformatics Core Meharry Medical College Nashville TN 37208 USA Department of Biochemistry and Cancer Biology Meharry Medical College Nashville TN 37208 USA Department of Medicine Vanderbilt University Medical Center Nashville TN 37232 USA Department of Life and Physical Sciences Fisk University Nashville TN 37208 USA Department of Pathology Anatomy and Cell Biology Meharry Medical College Nashville TN 37208 USA College of Computing and Technology Lipscomb University 1 University Park Drive Nashville TN 37204 USA Vanderbilt University Medical Center Nashville TN 37232 USA Office of Research Middle Tennessee State University 1301 East Main St Murfreesboro TN 37132 USA Department of Microbiology and Immunology Faculty of Pharmacy Cairo University Cairo Egypt Department of Biological Sciences Texas Tech University Lubbock TX 79409 USA Clinical Operations Department Ray-Clinical Research Organization Giza Egypt Genomic and Systems Biology Bioscience Division Argonne National Laboratory Argonne IL 60439 USA Research Institute of Ophthalmology Giza Egypt Department of Surgery University of Chicago Chicago IL 60637 USA Microbial Ecology Group Argonne National Laboratory Argonne IL 60439 USA Department of Veterinary Science University of Kentucky Lexington KY 40536 USA Department of Reproduction Faculty of Veterinary Medicine University of Gent Merelbeke Belgium Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville KY 40292 USA Department of Veterinary Science University of Kentucky Lexington KY 40506 USA Genomics GPS Guilford CT 06437 USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville KY 40292 USA Department of Pharmacology & Toxicology University of Louisville Louisville KY 40202 USA Department of Medicine University of Louisville

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White Paper on Enabling Global Image Data Sharing in the Life Sciences

arXiv

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arXiv 2024年

作者： Bajcsy, Peter Bhattiprolu, Sreenivas Börner, Katy Cimini, Beth A. Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje National Institute of Standards and Technology GaithersburgMD20899 United States ZEISS Microscopy Customer Center DublinCA94568 United States Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Imaging Platform Broad Institute CambridgeMA United States Electron Microscopy Science Technology Platform Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom European Molecular Biology Laboratory Heidelberg Germany Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Department of Radiology and Committee on Medical Physics University of Chicago ChicagoIL United States National Imaging Facility Brisbane Australia European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom National Center for Advancing Translational Science National Institutes of Health Rockville United States Allen Institute for Cell Science Seattle United States Fondazione Human Technopole Milan Italy Stanford University California US and SciLifeLab KTH Royal Institute of Technology Stockholm Sweden Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States RIKEN Center for Biosystems Dynamics Research Kobe Japan Department of Biomedical Informatics Department of Radiology University of Arkansas for Medical Sciences Little RockAR72205 United States Divisions of Computational Biology and Molecular Cell and Developmental Biology University of Dundee United Kingdom Sage Bionetworks SeattleWA United States

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ‘ideal world scenario’ for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled "Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data", which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. © 2024, CC BY-NC-SA.

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Nat Methods:利用CRISPR-Cas9组合筛选发现癌症弱点

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现代生物医学进展 2017年第17期17卷 I0001-I0001页

作者： John Paul Shen, Ana Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Daniel Pekin, Alex N Beckett, Kyle Salinas Sanchez, Jason F Kreisberg, Trey Ideker John Paul Shen, Trey Ideker, Prashant Mali John Paul Shen, Dongxin Zhao, Dan Du, Assen Roguev, Jason F Kreisberg, Nevan Krogan, Lei Qi, Trey Ideker, Prashant Mali Dongxin Zhao, Chih-Chung Kuo, Prashant Mali Roman Sasik, Amanda Birmingham, Aaron N Chang, Trey Ideker Jens Luebeck, Alex Thomas Alex Thomas, Chih-Chung Kuo, Nathan E Lewis Dan Du Assen Roguev, Nevan Krogan Nathan E Lewis Lei Qi Department of Medicine Division of Genetics University of California San Diego La Jolla California USA Moores UCSD Cancer Center La Jolla California USA The Cancer Cell Map Initiative (CCMI) La Jolla and San Francisco California USA Department of Bioengineering University of California San Diego La Jolla California USA Center for Computational Biology & Bioinformatics University of California San Diego La Jolla California USA Bioinformatics & Systems Biology Program University of California San Diego La Jolla California USA Novo Nordisk Center for Biosustainability at the University of California San Diego La Jolla California USA Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research Department of Cell and Tissue Biology University of California San Francisco San Francisco California USA Department of Cellular and Molecular Pharmacology University of California San Francisco San Francisco California USA Department of Pediatrics University of California San Diego La Jolla California USA Department of Bioengineering Stanford University Stanford California USA.

导致癌症的基因突变也会削弱癌细胞，从而使得人们有机会开发选择性地杀死它们同时不影响正常细胞的药物。这一概念被称作”合成致死性（synthetic lethality）”，这是因为这些药物仅对发生突变的（或者说合成的）细胞是致命性的。在... 详细信息

导致癌症的基因突变也会削弱癌细胞，从而使得人们有机会开发选择性地杀死它们同时不影响正常细胞的药物。这一概念被称作”合成致死性（synthetic lethality）”，这是因为这些药物仅对发生突变的（或者说合成的）细胞是致命性的。在一项新的研究中。来自美国加州大学圣地亚哥分校、加州大学旧金山分校、斯坦福大学和癌细胞图谱项目（Cancer Cell Map Initiative）的研究人员开发出一种新的方法来寻找合成致死性的基因组合。

关键词：组合筛选癌症美国加州大学利用基因突变斯坦福大学癌细胞正常细胞

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Modulating motor cortical oscillation with coordinated reset multifocal transcranial magnetic stimulation

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Journal of neurophysiology 2023年第5期129卷 1061-1071页

作者： Kai-Hsiang Stanley Chen Tzu-Kang Hung Rupesh Kumar Chikara Yu-Ting Kuo Yi-Ping Liu Yan-Siou Dong Yih-Ru Wu Li-Wei Ko Ying-Zu Huang Robert Chen Department of Neurology National Taiwan University Hospital Hsinchu Branch Hsinchu Taiwan. Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan. Jane and John Justin Neurosciences Center Cook Children's Health Care System Fort Worth Texas United States. Department of Bioengineering University of Texas at Arlington Arlington Texas United States. Department of Neurology Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taoyuan Taiwan. Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan. Neuroscience Research Center and Department of Neurology Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taoyuan Taiwan. Institute of Cognitive Neuroscience College of Health Science and Technology Taoyuan Taiwan. Krembil Research Institute University Health Network Toronto Ontario Canada. Division of Neurology Department of Medicine University of Toronto Toronto Ontario Canada. Edmond J. Safra Program in Parkinson's Disease University Health Network Toronto Ontario Canada.

According to the theory of coordinated reset (CR) stimulation, multifocal bursts of stimuli delivered in a random order with a specific interval may reduce the resonance power of the oscillatory generator in the epicenter. We develop a noninvasive coordinated multifocal burst stimulation (COMBS) with three repetitive transcranial stimulation machines based on CR theory to modulate the target frequency in the primary motor cortex and to assess its effect on motor cortical excitability in separate experiments. Electroencephalography and electromyography were recorded in 16 healthy participants during a finger-tapping task, both before and after the intervention. The resting oscillatory power at the targeted frequency was not changed by COMBS. α-Band power was increased in both preparation and movement stages and the low β-band power was increased in the movement stage of the finger tapping task. The extent of low β-band event-related desynchronization was reduced by COMBS. There were no changes in reaction time, but there was a trend for a reduced error rate after COMBS. In another 14 healthy participants, there were no significant changes in cortical excitability before and after COMBS measured by rest motor threshold, short interval intracortical inhibition, short interval intracortical facilitation, and cortical silent period. The result indicates that COMBS may modify the cortical oscillatory power and its perturbation within specific movement stage. This is the first study, to our knowledge, to apply coordinated reset (CR) neuromodulation to the motor cortex with three repetitive transcranial magnetic stimulation (rTMS) stimulators to assess its effect on cortical oscillation. The results revealed enhancement of α-band power specifically in preparation and movement stages and low β-band power in the movement stage of a motor task. It postulated that CR stimulation may modify the motor cortical oscillation in the specific movement stages.

关键词： coordinated reset event-related desynchronization low beta oscillation motor cortical excitability repetitive transcranial magnetic stimulation (rTMS)

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